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Author: Robert Mitchell Lloyd
Publisher: Thomas Telford
ISBN: 9780727728395
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
The field of civil engineering offers specific challenges to the higher education sector. Civil engineerings blend of management design and analysis requires people with a combination of academic and experimental knowledge and skill-based abilities.This volume brings together papers by leading practitioners in the field of learning technology, within the discipline of civil engineering, to facilitate the sharing of experience, knowledge and expertise.
Author: Robert Mitchell Lloyd
Publisher: Thomas Telford
ISBN: 9780727728395
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
The field of civil engineering offers specific challenges to the higher education sector. Civil engineerings blend of management design and analysis requires people with a combination of academic and experimental knowledge and skill-based abilities.This volume brings together papers by leading practitioners in the field of learning technology, within the discipline of civil engineering, to facilitate the sharing of experience, knowledge and expertise.
Author: Ivan Mutis
Publisher:
ISBN: 9780784414866
Category : Civil engineering
Languages : en
Pages : 340
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Book Description
The collection brings together new approaches to research in the use of computer-mediated learning technologies in civil engineering education.
Author: James-A. Goulet
Publisher: MIT Press
ISBN: 0262358018
Category : Computers
Languages : en
Pages : 298
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Book Description
An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.
Author: Hudson Jackson
Publisher: CRC Press
ISBN: 1000569209
Category : Technology & Engineering
Languages : en
Pages : 245
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Book Description
This book compiles proven strategies and information on civil engineering education and the skills necessary for successful practice of civil engineering such as critical thinking, design thinking, leadership, and communication skills. It also addresses other relevant topics including professional ethics, global perspectives, assessment, recruitment, retention, and more. It is designed so that each chapter can be used separately or in combination with other chapters to help enhance and foster student learning as well as development of skills required for engineering practice. Features Includes overviews of successful academic approaches for each topic including implementation examples in every chapter Explains how assessment and the resulting data can be used for holistic evaluation and improvement of student learning Addresses the complexities of moral and professional ethics in engineering Highlights the importance of adopting a global perspective and the successful strategies that have been used or considered in educating resilient, globally minded engineers Compendium of Civil Engineering Education Strategies: Case Studies and Examples serves as a useful guide for engineering faculty, practitioners, and graduate students considering a career in academia. Academic faculty and working professionals will find the content helpful as instructional and reference material in developing and assessing career skills. It is also useful for intellectually curious students who want a deeper understanding and appreciation of the need for professional development and life-long learning.
Author: Paresh Chandra Deka
Publisher: CRC Press
ISBN: 0429836651
Category : Computers
Languages : en
Pages : 211
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Book Description
Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises
Author: Johnson, Rick D.
Publisher: IGI Global
ISBN: 1799838129
Category : Psychology
Languages : en
Pages : 742
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Book Description
The subject of leadership and managerial psychology exists as a sub-branch of psychology within the fields of industrial and organizational psychology. There still appears to be ongoing debate regarding the core pathology for gaining managerial expertise in professional roles relative to having suitable leadership skills and managerial knowledge beyond the direct daily work involved in organizations. Professional organizations inherently include varied levels of sensitive human interactions, which further necessitates their management professionals to have leadership styles that are adjustable contingent on a given situation. Relative to this edited book, managerial psychology is being utilized in a way that may subsequently seek to develop a series of scientific theory principles where the focus is to develop managerial axioms that advance contemporary existing knowledge surrounding professional management logic. The Handbook of Research on Multidisciplinary Perspectives on Managerial and Leadership Psychology provides value uncovered by a collaboration of generalists and specialists who bring professional managerial and leadership opinions to light through narratives and research inclusive of fundamental theory principles that can be applied in practice and academia. This edited reference is focused on the enhancement of management research through managerial psychology while highlighting topics including business process knowledge, management in diverse discipline situations and professions, corporate leadership responsibility, leadership of self and others, and leadership psychology in a variety of different fields of work. This book is ideally designed for leadership and management professionals, academicians, students, and researchers in the fields of knowledge management, administrative sciences and management, leadership development, education, and organization development sub-branches or specialty practices.
Author: Ebner, Martin
Publisher: IGI Global
ISBN: 1615206795
Category : Education
Languages : en
Pages : 556
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Book Description
"This book evaluated the incorporation of technology into educational processes reviewing topics from primary and secondary school to higher education, from Second Life to wiki technology, from physical education to cultural learning"--Provided by publisher.
Author: Raymond Issa
Publisher:
ISBN: 9780784413616
Category : Building
Languages : en
Pages : 0
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Book Description
Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262369303
Category : Computers
Languages : en
Pages : 858
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Book Description
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Author: National Research Council
Publisher: National Academies Press
ISBN: 030914471X
Category : Education
Languages : en
Pages : 595
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Book Description
Engineering education in K-12 classrooms is a small but growing phenomenon that may have implications for engineering and also for the other STEM subjects-science, technology, and mathematics. Specifically, engineering education may improve student learning and achievement in science and mathematics, increase awareness of engineering and the work of engineers, boost youth interest in pursuing engineering as a career, and increase the technological literacy of all students. The teaching of STEM subjects in U.S. schools must be improved in order to retain U.S. competitiveness in the global economy and to develop a workforce with the knowledge and skills to address technical and technological issues. Engineering in K-12 Education reviews the scope and impact of engineering education today and makes several recommendations to address curriculum, policy, and funding issues. The book also analyzes a number of K-12 engineering curricula in depth and discusses what is known from the cognitive sciences about how children learn engineering-related concepts and skills. Engineering in K-12 Education will serve as a reference for science, technology, engineering, and math educators, policy makers, employers, and others concerned about the development of the country's technical workforce. The book will also prove useful to educational researchers, cognitive scientists, advocates for greater public understanding of engineering, and those working to boost technological and scientific literacy.
